An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records

  • Authors:

  • Jonathan S. Schildcrout;Melissa A. Basford;Jill M. Pulley;Daniel R. Masys;Dan M. Roden;Deede Wang;Christopher G. Chute;Iftikhar J. Kullo;David Carrell;Peggy Peissig;Abel Kho;Joshua C. Denny

  • Affiliations:

  • Department of Biostatistics, Vanderbilt University School of Medicine, United States and Department of Anesthesiology, Vanderbilt University School of Medicine, United States;Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University School of Medicine, United States;Department of Medicine, Vanderbilt University School of Medicine, United States;Department of Medicine, Vanderbilt University School of Medicine, United States and Department of Biomedical Informatics, Vanderbilt University School of Medicine, United States;Department of Medicine, Vanderbilt University School of Medicine, United States and Department of Pharmacology, Vanderbilt University School of Medicine, United States;Department of Medicine, Vanderbilt University School of Medicine, United States;Division of Biostatistics and Informatics, Mayo Clinic, United States;Division of Cardiovascular Diseases, Mayo Clinic, United States;Center for Health Studies, Group Health Cooperative, United States;Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, United States;Department of Internal Medicine, Northwestern University School of Medicine, United States;Department of Medicine, Vanderbilt University School of Medicine, United States and Department of Biomedical Informatics, Vanderbilt University School of Medicine, United States

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2010

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Abstract

We describe a two-stage analytical approach for characterizing morbidity profile dissimilarity among patient cohorts using electronic medical records. We capture morbidities using the International Statistical Classification of Diseases and Related Health Problems (ICD-9) codes. In the first stage of the approach separate logistic regression analyses for ICD-9 sections (e.g., ''hypertensive disease'' or ''appendicitis'') are conducted, and the odds ratios that describe adjusted differences in prevalence between two cohorts are displayed graphically. In the second stage, the results from ICD-9 section analyses are combined into a general morbidity dissimilarity index (MDI). For illustration, we examine nine cohorts of patients representing six phenotypes (or controls) derived from five institutions, each a participant in the electronic MEdical REcords and GEnomics (eMERGE) network. The phenotypes studied include type II diabetes and type II diabetes controls, peripheral arterial disease and peripheral arterial disease controls, normal cardiac conduction as measured by electrocardiography, and senile cataracts.